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Pharmacokinetic design optimization in children and estimation of maturation parameters: example of cytochrome P450 3A4

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Abstract

The aim of this work was to determine whether optimizing the study design in terms of ages and sampling times for a drug eliminated solely via cytochrome P450 3A4 (CYP3A4) would allow us to accurately estimate the pharmacokinetic parameters throughout the entire childhood timespan, while taking into account age- and weight-related changes. A linear monocompartmental model with first-order absorption was used successively with three different residual error models and previously published pharmacokinetic parameters (“true values”). The optimal ages were established by D-optimization using the CYP3A4 maturation function to create “optimized demographic databases.” The post-dose times for each previously selected age were determined by D-optimization using the pharmacokinetic model to create “optimized sparse sampling databases.” We simulated concentrations by applying the population pharmacokinetic model to the optimized sparse sampling databases to create optimized concentration databases. The latter were modeled to estimate population pharmacokinetic parameters. We then compared true and estimated parameter values. The established optimal design comprised four age ranges: 0.008 years old (i.e., around 3 days), 0.192 years old (i.e., around 2 months), 1.325 years old, and adults, with the same number of subjects per group and three or four samples per subject, in accordance with the error model. The population pharmacokinetic parameters that we estimated with this design were precise and unbiased (root mean square error [RMSE] and mean prediction error [MPE] less than 11% for clearance and distribution volume and less than 18% for ka), whereas the maturation parameters were unbiased but less precise (MPE < 6% and RMSE < 37%). Based on our results, taking growth and maturation into account a priori in a pediatric pharmacokinetic study is theoretically feasible. However, it requires that very early ages be included in studies, which may present an obstacle to the use of this approach. First-pass effects, alternative elimination routes, and combined elimination pathways should also be investigated.

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Authors and Affiliations

  1. Université Paris Descartes, Paris, France

    Marion Bouillon-Pichault, Vincent Jullien & Gérard Pons

  2. Inserm, U663, 75015, Paris, France

    Marion Bouillon-Pichault, Vincent Jullien & Gérard Pons

  3. Faculty of Medicine, University Paris Descartes, 75005, Paris, France

    Marion Bouillon-Pichault, Vincent Jullien & Gérard Pons

  4. RIPPS Network, Paris, France

    Marion Bouillon-Pichault, Vincent Jullien & Gérard Pons

  5. APHP, Groupe Hospitalier Cochin-Saint- Vincent De Paul, Paris, France

    Marion Bouillon-Pichault, Vincent Jullien & Gérard Pons

  6. INSERM, U738, Paris, France

    Caroline Bazzoli

  7. Université de Lyon, 69003, Lyon, France

    Michel Tod

  8. Faculté de Médecine Lyon Sud, Université Lyon 1, EA3738, CTO, 69600, Oullins, France

    Michel Tod

  9. Pharmacie, Hôpital de la Croix-Rousse, Hospices Civils de Lyon, Lyon, France

    Michel Tod

  10. Service de Pharmacologie Clinique, Groupe Hospitalier Cochin-Saint-Vincent de Paul 74–82 avenue Denfert-Rochereau, 75674, Paris Cedex 14, France

    Marion Bouillon-Pichault

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  1. Marion Bouillon-Pichault
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  2. Vincent Jullien
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  3. Caroline Bazzoli
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  4. Gérard Pons
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  5. Michel Tod
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Correspondence to Marion Bouillon-Pichault.

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Bouillon-Pichault, M., Jullien, V., Bazzoli, C. et al. Pharmacokinetic design optimization in children and estimation of maturation parameters: example of cytochrome P450 3A4. J Pharmacokinet Pharmacodyn 38, 25–40 (2011). https://doi.org/10.1007/s10928-010-9173-1

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  • DOI: https://doi.org/10.1007/s10928-010-9173-1

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